Utkarsh Bansal: Port unit tests to pytest

Utkarsh will port our complete unit tests
from nose
to pytest. This is a massive undertaking
for MDAnalysis with over 4000 individual tests. But we have great confidence in
him and he has started work already to ensure that we don’t have a drop in code
coverage during the transition. Newer projects under the MDAnalysis umbrella all
use pytests and we are happy to see the switch happening for MDAnalysis as well.
Utkarsh will blog continuously during the summer to let you know
how far the transition has come and how to best write unit-tests in python.

Utkarsh is currently pursuing a bachelors in Computer Science and Engineering
and will be graduating this summer. He hopes to learn new things about python
and testing in general this summer and is planning to continue his career as a
software developer.

Other NumFOCUS students

NumFOCUS is hosting 12 students this year for several of their supported and
affiliated projects. You can find out about the other
students
here.

MDAnalysis started almost 10 years ago
when Python was around version 2.4 and interfacing with existing C code was
mostly done by writing C-wrappers that directly used CPython. This legacy code
has hampered a speedy full transition to Python 3 and consequently MDAnalysis
lags behind the rest of the scientific Python community in fully supporting
Python 3.
Although about 80% of code passes unit tests in Python 3, we urgently need to
close the remaining 20% gap in order to support our user base and to safeguard
the long term viability of the project.

In the meantime we are busy porting our last Python 2.7 only C-extension, the
DCD Reader and Writer, to Cython. We now have a working Cython version that can
be used without MDAnalysis, similar to our XTC and TRR readers. Only a clean up
of the new Cython / DCD handling code and updated documentation is required. You
can check our progress here.

We have just released MDAnalysis version 0.16.0. This release contains new
features as well as bug fixes. Highlights are listed below but for more details
see the release notes.

This is the biggest release we ever had with tons of new features. It includes a
rewrite of the topology system, the work of our GSoC students Fiona Naughton
(@fiona-naughton) and John Detlefs (@jdetle), a complete new ensemble analysis
with encore and much more. In total 28
people contributed 1904 commits to this release. We closed 162 issues
and merged 199 pull requests.

Upgrade

You can upgrade with pip install --upgrade MDAnalysis . If you use the conda
package manager run conda update -c conda-forge mdanalysis

Noticable Changes

Rewrite of our internal topology representation

This is the change we are most exited about for this release. It brings
performance enhancements, makes maintaining the code easier, it’s easier to
extend and allowed us a simplification of the interface. We have previously
written about the details of
new topology system.

But with this change also a lot of deprecated functions have been removed. For
all of the deprecated functions the new replacements already exists and you will
get a warning with a suggested change. The easiest way to check if your scripts
will run without a problem after the update is to include the following on top
of it.

importwarningswarnings.filterwarnings('always',module='MDAnalysis')

This will print a warning for every deprecated function you are using together
with a short code snippet how your code has to be changed. If you do not want to
upgrade your existing scripts
we posted a guide how to use conda and
python environments to run different versions of MDAnalysis on the same
computer.

Attach arbitrary time series to your trajectories

Our GSoC student @fiona-naughton has implemented an auxillary reader to add
arbitrary time series to a universe. The time series are kept in sync with the
trajectory so it is possible to iterate through the trajectory and access the
auxiliary data corresponding to the current time step.

importMDAnalysisasmdafromMDAnalysisTests.datafilesimportPDB_sub_sol,XTC_sub_sol,XVG_BZ2# Create your universe as usual
universe=mda.Universe(PDB_sub_sol,XTC_sub_sol)# Attach an auxiliary time serie with the name `forces`
# In this example, the XVG file contains the force that applies to each atom.
universe.trajectory.add_auxiliary('forces',XVG_BZ2)# Itarete through your trajectory, the time serie is kept in sync
fortime_stepinuniverse.trajectory:print(time_step.aux.forces)# The first element of each array is the time in picoseconds.
# The next elements are the other columns of the XVG file.

This feature is still in its beginning and will be expanded in future releases. You can
follow the conversation on the initial issue or on the pull request.
So far, only the XVG format used by gromacs and grace are supported. Open an issue
if you need support for other time series formats.

importmatplotlib.pyplotaspltimportMDAnalyisasmdafromMDAnalysis.analysis.pcaimportPCAfromMDAnalyisTests.datafilesimportPSF,DCDplt.style.use('ggplot')u=mda.Universe(PSF,DCD)pca=PCA(u,select='protein and name CA',verbose=True).run()reduced_data=pca.transform(ca,n_components=2)f,ax=plt.subplots()ax.plot(d[:,0],d[:,1],'o')ax.set(xlabel=r'PC$_1$ [$\AA$]',ylabel=r'PC$_2$ [$\AA$]',title='PCA of ADK')

Convenience functions to create a new analysis

A while back we introduced a new frame work for analysis to unify the API for
the different analysis methods we offer. With this release we also add a new
class AnalysisFromFunction to make it easier to calculate observables from a
simulation. Now code like this with a handwritten loop.

This class also takes arguments to adjust the iteration (start,stop,step)
and you can add verbosity with verbose=True . You will also profit from any
performance improvements in the analysis class in the future without changing
your code. If you have a specific observable that you want to calculate several
times you can also create a new analysis class with analysis_class like this.

Speed improvements in RMSD

Thanks for work from our NSF REU student @rbrtdlgd our RMSD calculations are about 40% faster now.
If you are using the low-level qcprot algorithm yourself instead of our provided
wrappers you have to change your code since the API has changed. For more see
the release notes.

MemoryReader: Reading trajectories from memory

MDAnalysis typically reads trajectories from files on-demand, so that it can efficiently deal with large trajectories - even those that do not fit in memory. However, in some cases, both for convenience and for efficiency, it can be an advantage to work with trajectories directly in memory. In this release, we have introduced a MemoryReader, which makes this possible. This Reader has been originally implemented in the encore package.

The MemoryReader works with numpy arrays, using the same format as that used by for instance DCDReader.timeseries(). You can create a Universe directly from such an array:

importnumpyasnpfromMDAnalysisimportUniversefromMDAnalysisTests.datafilesimportDCD,PSFfromMDAnalysis.coordinates.memoryimportMemoryReader# Create a Universe using a DCD reader
universe=Universe(PSF,DCD)# Create a numpy array with random coordinates (100 frames) for the same topology
coordinates=np.random.uniform(size=(100,universe.atoms.n_atoms,3)).cumsum(0)# Create a new Universe directly from these coordinates
universe2=Universe(PSF,coordinates,format=MemoryReader)

The MemoryReader will work just as any other reader. In particular, you can iterate over it as usual, or use the .timeseries() method to retrieve a reference to the raw array:

coordinates_fac=universe2.trajectory.timeseries(format='fac')

Certain operations can be speeded up by moving a trajectory to memory, and we have therefore
added functionality to directly transfer any existing trajectory to a MemoryReader using Universe.transfer_to_memory: